Prediction of the effects of preparation conditions on pervaporation performances of polydimethylsiloxane(PDMS)/ceramic composite membranes by backpropagation neural network and genetic algorithm

Separation and Purification Technology

Volumn 89, Issue , 2012, Pages 142-146

  Tan, Ming ^a   He, Gaohong ^a   Li, Xiangcun ^a   Liu, Yuanfa ^a   Dong, Chunxu ^a   Feng, Jinghai ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Backpropagation; Genetic algorithm; Neural network; Pervaporation; Preparation condition

Indexed keywords

BACK-PROPAGATION NEURAL NETWORKS; CONNECTION WEIGHTS; CROSS LINKING AGENTS; DIP COATING; HYBRID MODEL; MEMBRANE FLUXES; MEMBRANE PERFORMANCE; NON-LINEAR RELATIONSHIPS; PERVAPORATION MEMBRANES; POLYDIMETHYLSILOXANE PDMS; PREPARATION CONDITIONS; PREPARATION PROCESS; RESPONSE SURFACE METHODOLOGY; STANDARD DEVIATION; TRIAL-AND-ERROR METHOD;

BACKPROPAGATION; CERAMIC MATERIALS; CERAMIC MEMBRANES; COMPOSITE MEMBRANES; ETHANOL; EVAPORATION; FORECASTING; GENETIC ALGORITHMS; MEMBRANES; MICROCHANNELS; NEURAL NETWORKS; PERVAPORATION;

SILICONES;

EID: 84862793907     PISSN: 13835866     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.seppur.2012.01.011     Document Type: Article

References (39)

1. C.-L. Chang, and M.-S. Chang Preparation of multi-layer silicone/PVDF composite membranes for pervaporation of ethanol aqueous solutions J. Membr. Sci. 238 2004 117 122
2. H. Kim, H.-G. Kim, S. Kim, and S.S. Kim PDMS-silica composite membranes with silane coupling for propylene separation J. Membr. Sci. 344 2009 211 218
3. D.C. Mothgomery Design and Analysis of Experiment 1997 John Wiley & Sons, Inc. New York, USA
4. F. Xiangli, W. Wei, Y. Chen, W. Jin, and N. Xu Optimization of preparation conditions for polydimethylsiloxane (PDMS)/ceramic composite pervaporation membranes using response surface methodology J. Membr. Sci. 311 2008 23 33
5. S. Yi, Y. Su, B. Qi, Z. Su, and Y. Wan Application of response surface methodology and central composite rotatable design in optimizing the preparation conditions of vinyltriethoxysilane modified silicalite/polydimethylsiloxane hybrid pervaporation membranes Sep. Purif. Technol. 71 2010 252 262
6. R.H. Myers, D.C. Montgomery, and C.M. Anderson-Cook Response Surface Methodology: Process and Product Optimization Using Designed Experiments 2009 John Wiley & Sons, Inc. New York, USA
7. K.M. Carley, N.Y. Kamneva, and J. Reminga CASOS Technical report: Response surface methodology 2004 Carnegie Mellon University Pittsburgh, USA
8. A.F. Ismail, and P.Y. Lai Development of defect-free asymmetric polysulfone membranes for gas separation using response surface methodology Sep. Purif. Technol. 40 2004 191 207
9. A. Idris, F. Kormin, and M.Y. Noordin Application of response surface methodology in describing the performance of thin film composite membrane Sep. Purif. Technol. 49 2006 271 280
10. A.I. Khuri, and J.A. Cornell Response surfaces: designs and analyses 1996 Marcel Dekker New York, USA
11. M. Al-Abri, and N. Hilal Artificial neural network simulation of combined humic substance coagulation and membrane filtration Chem. Eng. J. 141 2008 27 34
12. H. Al-Zoubi, N. Hilal, N.A. Darwish, and A.W. Mohammad Rejection and modelling of sulphate and potassium salts by nanofiltration membranes: neural network and Spiegler-Kedem model Desalination 206 2007 42 60
13. E.J. Molga Neural network approach to support modelling of chemical reactors: problems, resolutions, criteria of application Chem. Eng. Process. 42 2003 675 695
14. R. Hecht-Nielsen, Theory of the backpropagation neural network, in: IJCNN, International Joint Conference on Neural Networks, 1989, 593-605.
15. Q.-F. Liu, S.-H. Kim, and S. Lee Prediction of microfiltration membrane fouling using artificial neural network models Sep. Purif. Technol. 70 2009 96 102
16. G.R. Shetty, and S. Chellam Predicting membrane fouling during municipal drinking water nanofiltration using artificial neural networks J. Membr. Sci. 217 2003 69 86
17. S. Chellam Artificial neural network model for transient crossflow microfiltration of polydispersed suspensions J. Membr. Sci. 258 2005 35 42
18. B. Sarkar, A. Sengupta, S. De, and S. DasGupta Prediction of permeate flux during electric field enhanced cross-flow ultrafiltration - a neural network approach Sep. Purif. Technol. 65 2009 260 268
19. R.S. Sexton, and J.N.D. Gupta Comparative evaluation of genetic algorithm and backpropagation for training neural networks Inform. Sciences 129 2000 45 59
20. P.A. Castillo, J.J. Merelo, A. Prieto, V. Rivas, and G. Romero G-Prop: global optimization of multilayer perceptrons using GAs Neurocomputing 35 2000 149 163
21. A. Saengrung, A. Abtahi, and A. Zilouchian Neural network model for a commercial PEM fuel cell system J. Power Sources 172 2007 749 759
22. M.T. Hagan, H.B. Demuth, and M. Beale Neural Network Design 1996 PWS publishing company, a division of Thomson learning Boston, USA
23. U. Kesgin Genetic algorithm and artificial neural network for engine optimisation of efficiency and NOx emission Fuel 83 2004 885 895
24. S.S. Madaeni, N.T. Hasankiadeh, A.R. Kurdian, and A. Rahimpour Modeling and optimization of membrane fabrication using artificial neural network and genetic algorithm Sep. Purif. Technol. 76 2010 33 43
25. Y. Mu, and H.-Q. Yu Simulation of biological hydrogen production in a UASB reactor using neural network and genetic algorithm Int. J. Hydrogen Energy 32 2007 3308 3314
26. F. Xiangli, Y. Chen, W. Jin, and N. Xu Polydimethylsiloxane (PDMS)/ceramic composite membrane with high flux for pervaporation of ethanol/water mixtures Ind. Eng. Chem. Res. 46 2007 2224 2230
27. M. Torres, C. Hervas, and F. Amador Approximating the sheep milk production curve through the use of artificial neural networks and genetic algorithms Comput. Oper. Res. 32 2005 2653 2670
28. C. Aydiner, I. Demir, and E. Yildiz Modeling of flux decline in crossflow microfiltration using neural networks: the case of phosphate removal J. Membr. Sci. 248 2005 53 62
29. C. Shen, L. Wang, and Q. Li Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method J. Mater. Process. Technol. 183 2007 412 418
30. Y.-D. Ko, P. Moon, C.E. Kim, M.-H. Ham, J.-M. Myoung, and I. Yun Modeling and optimization of the growth rate for ZnO thin films using neural networks and genetic algorithms Expert Syst. Appl. 36 2009 4061 4066
31. A. Blanco, M. Delgado, and M.C. Pegalajar A real-coded genetic algorithm for training recurrent neural networks Neural networks 14 2001 93 105
32. R.-Q. Fu, T.-W. Xu, and Z.-X. Pan Modelling of the adsorption of bovine serum albumin on porous polyethylene membrane by back-propagation artificial neural network J. Membr. Sci. 251 2005 137 144
33. W.R. Bowen, M.G. Jones, and H.N.S. Yousef Dynamic ultrafiltration of proteins - a neural network approach J. Membr. Sci. 146 1998 225 235
34. C. Teodosiu, O. Pastravanu, and M. Macoveanu Neural network models for ultrafiltration and backwashing Water Res. 34 2000 4371 4380
35. N.M. Jenny, and G. Foley Dead-end filtration of yeast suspensions: correlating specific resistance and flux data using artificial neural networks J. Membr. Sci. 281 2006 325 333
36. J.X. Guo, G.J. Zhang, W. Wu, S.L. Ji, Z.P. Qin, and Z.Z. Liu Dynamically formed inner skin hollow fiber polydimethylsiloxane/polysulfone composite membrane for alcohol permselective pervaporation Chem. Eng. J. 158 2010 558 565
37. X.-P. Wang Modified alginate composite membranes for the dehydration of acetic acid J. Membr. Sci. 170 2000 71 79
38. M. Hyder, R. Huang, and P. Chen Composite poly(vinyl alcohol)- poly(sulfone) membranes crosslinked by trimesoyl chloride: characterization and dehydration of ethylene glycol-water mixtures J. Membr. Sci. 326 2009 363 371
39. J. Chen, J. Li, J. Chen, Y. Lin, and X. Wang Pervaporation separation of ethyl thioether/heptane mixtures by polyethylene glycol membranes Sep. Purif. Technol. 66 2009 606 612